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Manufacturing

AI for Metals Manufacturing Operations

AI adoption in manufacturing is accelerating. But the use cases that actually move the needle are far more specific than most “Industry 4.0” content lets on.

For metals manufacturers: service centres, stockholders, fabricators, and structural steel processors. The problems worth solving are concrete. Cutting scrap on long products, bar, and structural sections. Managing stock across dozens of grades, dimensions, and forms without losing track of what’s in the yard. Keeping mill certificate records traceable and audit-ready. Hitting delivery commitments when the schedule changes at noon and three jobs need replanning before the shift ends.

AI solves these problems without ripping out your ERP or running a six-month implementation project. Tools like Cutting Plans, MillCert Reader, and Metals Manager work on top of what you already use: your existing ERP, CSV exports, spreadsheets, email order intake. They add intelligence where you’re currently burning hours or material. You can be live in a day from a CSV upload. You start with one product family or one process. You scale when you’ve seen it work.

Posts here cover practical AI use cases for metals operations: cutting optimisation, certificate automation, real-time inventory, and what an AI-assisted planning workflow actually looks like on the shop floor.

No buzzwords. No theory. Just what works for metal.

MLOps is like process engineering for Data Science

The goal of MLOps is to streamline the development, deployment, and operation of machine learning models, by supporting their building, testing, releasing, monitoring, performance tracking, reusing, maintenance and governance, joining the efforts of Data Science and IT teams under a shared focus.

Read More: MLOps is like process engineering for Data Science

Digital Supply Chains and why you need one

Adopting a Digital Supply Chain is big step towards achieving bigger goals like faster, more precise processes with visibility for the whole company. Digital Supply Chains can be hard to implement, but having a strategy is key. Most manufacturers already have some sort of data from ERP systems so building a Digital Supply Chain can begin with embracing more digital integrations with the ERP system and improving governance of the platform. It is very important to ensure that the implementation of a Digital Supply Chain is taken as seriously as other core business processes.

Read More: Digital Supply Chains and why you need one

Digital Twins and AI for manufacturers

Digital Twins are virtual replicas of real-world systems enabling low-cost modelling of the factory floor to help optimise processes. Combined with AI the Digital Twin can support improved forecasting, dynamic optimisation, and more.

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Microsoft Build 2021 announcements for manufacturers

Microsoft Build 2021 is kicking off and whilst it tends not to be as announcement heavy as Microsoft Ignite, it’s still got some great things for manufacturers to pay attention to. This post gives a quick run down of the most important stuff you need to read about. Enjoy!

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Smart Manufacturing is all about Real-Time Data Analytics

Manufacturers are spending far too much time on data entry or looking at stale data which is hindering growth. Here are seven ways real-time data can turn things around.

Read More: Smart Manufacturing is all about Real-Time Data Analytics

Announcing GoSmarter In A Day

Our GoSmarter In A Day workshops solve one problem like processing Accounts Payable incoming invoices to demonstrate the value such automation can bring.

Read More: Announcing GoSmarter In A Day

Data Champions are critical to your success in digitally transforming

Manufacturers need Data Champions to help them succeed in today’s digital world. To learn more about how you can find your Data Champion for your team or company, read on!

Read More: Data Champions are critical to your success in digitally transforming

Checklists make everything better - including responsible AI!

Building a checklist to cover the six key areas of responsible AI can drive better outcomes and returns for your business. Start with the list of areas as a discussion aid and grow your checklist with use.

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Using AI to make your warehouse operations smarter

You can benefit from lower overheads by using AI to manage stock levels, whilst building a more scalable warehouse operation that is safer for employees, using AI.

Read More: Using AI to make your warehouse operations smarter

Detecting defects with AI - a computer vision challenge

With the pace of output on machines ever increasing, quality control becomes a lot tougher. Using AI to detect defective products sooner can help scale your quality processes and avoid significant stops.

Read More: Detecting defects with AI - a computer vision challenge

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